Data modelling recipes for SARS-CoV-2 wastewater-based epidemiology.

Wastewater based epidemiology is recognized as one of the monitoring pillars, providing essential information for pandemic management. Central in the methodology are data modelling concepts for both communicating the monitoring results but also for analysis of the signal. It is due to the fast development of the field that a range of modelling concepts are used but without a coherent framework. This paper provides for such a framework, focusing on robust and simple concepts readily applicable, rather than applying latest findings from e.g., machine learning. It is demonstrated that data preprocessing, most important normalization by means of biomarkers and equal temporal spacing of the scattered data, is crucial. In terms of the latter, downsampling to a weekly spaced series is sufficient. Also, data smoothing turned out to be essential, not only for communication of the signal dynamics but likewise for regressions, nowcasting and forecasting. Correlation of the signal with epidemic indicators requires multivariate regression as the signal alone cannot explain the dynamics but - for this case study - multiple linear regression proofed to be a suitable tool when the focus is on understanding and interpretation. It was also demonstrated that short term prediction (7 days) is accurate with simple models (exponential smoothing or autoregressive models) but forecast accuracy deteriorates fast for longer periods.

Smart water campus - a testbed for smart water applications.

The Internet of Things concept includes low-cost sensors in combination with innovative wireless communication technology, supporting a large-scale implementation of measurement equipment in the field of urban water infrastructure (UWI). At present, the potentials of such smart solutions are often unclear, making it difficult for decision-makers to justify investments. To address this shortcoming, the Smart Campus is represented as an innovative testbed for smart and data-driven applications in the field of network-based UWI. During the last few years, the campus area of the University of Innsbruck has been comprehensively equipped with a variety of low-cost sensors for monitoring and controlling the UWI in high resolution (1-15 min). The experiences showed that the quality of service is influenced by the choice of communication technology and the installation location, thereby affecting the desired applications. Additionally, water distribution and urban drainage network including nature-based solutions have been integrated into an overall monitored system extended by measures to involve the urban population. This integrative approach allows the usage of synergies for the implementation and supports cross-system improvements (e.g., smart rainwater harvesting). However, an integration of different participants also implies new requirements for the project team (e.g., including social science).

On the effect of the inlet configuration for anaerobic digester mixing.

Sludge recirculation mixing in anaerobic digesters is essential for the stable operation of the digestion process. While often neglected, the configuration of the sludge inlet has a substantial influence on the efficiency of the mixing process. The fluid is either injected directly into the enclosed fluid domain or splashes onto the free surface of the slurry flow. In this paper, the aim was to investigate the effect of the inlet configuration by means of computational fluid dynamics-using ANSYS Fluent. Single-phase and multi-phase models are applied for a submerged and splashing inlet, respectively. To reduce the high computational demand, we also develop surrogate single-phase models for the splashing inlet. The digester mixing is analyzed by comparing velocity contours, velocity profiles, mixing time and dead volume. The non-Newtonian characteristics of the sludge is considered, and a [Formula: see text] model is employed for obtaining turbulence closure. Our method is validated by means of a previous study on the same geometry. Applying a submerged inlet configuration, the resulting dead volume in the tank is estimated around 80 times lower than for the case of a splashing inlet. Additionally, by emulating the multi-phase model for splashing inlet configurations with a single-phase one, the simulation clock time reduced to 15%.

Efficient integration of IoT-based micro storages to improve urban drainage performance through advanced control strategies.

The smart rain barrel (SRB) consists of a conventional RB with storage volumes between 200 and 500 L, which is extended by a remotely (and centrally) controllable discharge valve. The SRB is capable of releasing stormwater prior to precipitation events by using high-resolution weather forecasts to increase detention capacity. However, as shown in a previous work, a large-scale implementation combined with a simultaneous opening of discharge valves clearly reduced the effectiveness. The aim of this work was to systematically investigate different control strategies for wet weather by evaluating their impact on sewer performance. For the case study, an alpine municipality was hypothetically retrofitted with SRBs (total additional storage volume of 181 m3). The results showed that combined sewer overflow (CSO) volume and subsequently pollution mass can be reduced by between 7 and 67% depending on rain characteristics (e.g., rain pattern, amount of precipitation) and an applied control strategy. Effectiveness of the SRBs increases with lower CSO volume, whereas more advanced control strategies based on sewer conditions can clearly improve the system's performance compared to simpler control strategies. For higher CSO volume, the SRBs can postpone the start of an CSO event, which is important for a first-flush phenomenon.

Data filtering methods for SARS-CoV-2 wastewater surveillance.

In the case of SARS-CoV-2 pandemic management, wastewater-based epidemiology aims to derive information on the infection dynamics by monitoring virus concentrations in the wastewater. However, due to the intrinsic random fluctuations of the viral signal in wastewater caused by several influencing factors that cannot be determined in detail (e.g. dilutions; number of people discharging; variations in virus excretion; water consumption per day; transport and fate processes in sewer system), the subsequent prevalence analysis may result in misleading conclusions. It is thus helpful to apply data filtering techniques to reduce the noise in the signal. In this paper we investigate 13 smoothing algorithms applied to the virus signals monitored in four wastewater treatment plants in Austria. The parameters of the algorithms have been defined by an optimization procedure aiming for performance metrics. The results are further investigated by means of a cluster analysis. While all algorithms are in principle applicable, SPLINE, Generalized Additive Model and Friedman's Super Smoother are recognized as superior methods in this context (with the latter two having a tendency to over-smoothing). A first analysis of the resulting datasets indicates the positive effect of filtering to the correlation of the viral signal to monitored incidence values.

Virtual reality in urban water management: communicating urban flooding with particle-based CFD simulations.

For communicating urban flood risk to authorities and the public, a realistic three-dimensional visual display is frequently more suitable than detailed flood maps. Virtual reality could also serve to plan short-term flooding interventions. We introduce here an alternative approach for simulating three-dimensional flooding dynamics in large- and small-scale urban scenes by reaching out to computer graphics. This approach, denoted 'particle in cell', is a particle-based CFD method that is used to predict physically plausible results instead of accurate flow dynamics. We exemplify the approach for the real flooding event in July 2016 in Innsbruck.

What can we learn from a 500-year event? Experiences from urban drainage in Austria.

Urban drainage systems are designed to capture the runoff for a certain return period of a design rainfall event. Typically, numerical models are used, which are calibrated by comparing model response and measured system performance. The applicability of such models to predict the system behaviour under extreme events is unclear, as usually then no data are available. This paper describes the analysis of an extreme rainfall event in the year 2016. The event is characterized by a very short duration and very high rainfall intensities. The maximum-recorded rainfall peak was 47.1 mm rainfall within 10 min, which corresponds to a return period of 500 years. The event caused local flooding on streets, interruptions of traffic and damages in buildings. In order to improve the flood resilience of the city, the event was analysed with an existing 1D hydrodynamic model of the sewer system. Model results were compared to water level measurements in the drainage system and citizen observations of surface flooding (gathered from social media and citizen reports). Although the hydrodynamic model could reproduce water level measurements in parts of the system, the plausibility check using descriptive data showed that the model failed to predict flooding in some areas.

On the sensitivity of geospatial low impact development locations to the centralized sewer network.

In the future, infrastructure systems will have to become smarter, more sustainable, and more resilient requiring new methods of urban infrastructure design. In the field of urban drainage, green infrastructure is a promising design concept with proven benefits to runoff reduction, stormwater retention, pollution removal, and/or the creation of attractive living spaces. Such 'near-nature' concepts are usually distributed over the catchment area in small scale units. In many cases, these above-ground structures interact with the existing underground pipe infrastructure, resulting in hybrid solutions. In this work, we investigate the effect of different placement strategies for low impact development (LID) structures on hydraulic network performance of existing drainage networks. Based on a sensitivity analysis, geo-referenced maps are created which identify the most effective LID positions within the city framework (e.g. to improve network resilience). The methodology is applied to a case study to test the effectiveness of the approach and compare different placement strategies. The results show that with a simple targeted LID placement strategy, the flood performance is improved by an additional 34% as compared to a random placement strategy. The developed map is easy to communicate and can be rapidly applied by decision makers when deciding on stormwater policies.

Morphogenesis of Urban Water Distribution Networks: A Spatiotemporal Planning Approach for Cost-Efficient and Reliable Supply.

Cities and their infrastructure networks are always in motion and permanently changing in structure and function. This paper presents a methodology for automatically creating future water distribution networks (WDNs) that are stressed step-by-step by disconnection and connection of WDN parts. The associated effects of demand shifting and flow rearrangements are simulated and assessed with hydraulic performances. With the methodology, it is possible to test various planning and adaptation options of the future WDN, where the unknown (future) network is approximated via the co-located and known (future) road network, and hence different topological characteristics (branched vs. strongly looped layout) can be investigated. The reliability of the planning options is evaluated with the flow entropy, a measure based on Shannon's informational entropy. Uncertainties regarding future water consumption and water loss management are included in a scenario analysis. To avoid insufficient water supply to customers during the transition process from an initial to a final WDN state, an adaptation concept is proposed where critical WDN components are replaced over time. Finally, the method is applied to the drastic urban transition of Kiruna, Sweden. Results show that without adaptation measures severe performance drops will occur after the WDN state 2023, mainly caused by the disconnection of WDN parts. However, with low adaptation efforts that consider 2-3% pipe replacement, sufficient pressure performances are achieved. Furthermore, by using an entropy-cost comparison, the best planning options are determined.

Investigating the interactions of decentralized and centralized wastewater heat recovery systems.

In the urban water cycle there are different sources for extracting energy. In addition to potential and chemical energy in the wastewater, thermal energy can also be recovered. Heat can be recovered from the wastewater with heat exchangers that are located decentralized and/or centralized at several locations throughout the system. It can be recovered directly at the source (e.g. in the showers and bathrooms), at building block level (e.g. warm water tanks collecting all grey water), in sewers or at the wastewater treatment plant. However, an uncoordinated installation of systems on such different levels can lead to competing technologies. To investigate these interactions, a modelling environment is set up, tested and calibrated based on continuous sewer temperature and flow measurements. With that approach different heat recovery scenarios on a household level (decentralized) and of in-sewer heat recovery (centralized) are investigated. A maximum performance drop of 40% for a centralized energy recovery system was estimated when all bathrooms are equipped with decentralized recovery systems. Therefore, the proposed modelling approach is suitable for testing different future conditions and to identify robust strategies for heat recovery systems from wastewater.

Info-Gap robustness pathway method for transitioning of urban drainage systems under deep uncertainties.

In the urban water cycle, there are different ways of handling stormwater runoff. Traditional systems mainly rely on underground piped, sometimes named 'gray' infrastructure. New and so-called 'green/blue' ambitions aim for treating and conveying the runoff at the surface. Such concepts are mainly based on ground infiltration and temporal storage. In this work a methodology to create and compare different planning alternatives for stormwater handling on their pathways to a desired system state is presented. Investigations are made to assess the system performance and robustness when facing the deeply uncertain spatial and temporal developments in the future urban fabric, including impacts caused by climate change, urbanization and other disruptive events, like shifts in the network layout and interactions of 'gray' and 'green/blue' structures. With the Info-Gap robustness pathway method, three planning alternatives are evaluated to identify critical performance levels at different stages over time. This novel methodology is applied to a real case study problem where a city relocation process takes place during the upcoming decades. In this case study it is shown that hybrid systems including green infrastructures are more robust with respect to future uncertainties, compared to traditional network design.

Lost in calibration: why people still do not calibrate their models, and why they still should - a case study from urban drainage modelling.

From a scientific point of view, it is unquestioned that numerical models for technical systems need to be calibrated. However, in sufficiently calibrated models are still used in engineering practice. Case studies in the scientific literature that deal with urban water management are mostly large cities, while little attention is paid to the differing boundary conditions of smaller municipalities. Consequently, the aim of this paper is to discuss the calibration of a hydrodynamic model of a small municipality (15,000 inhabitants). To represent the spatial distribution of precipitation, three distributed rain gauges were used for model calibration. To show the uncertainties imminent to the calibration process, 17 scenarios, differing in assumptions for calibration, were distinguished. To compare the impact of the different calibration scenarios on actual design values, design rainfall events were applied. The comparison of the model results using the different typical design storm events from all the surrounding data points showed substantial differences for the assessment of the sewers regarding urban flooding, emphasizing the necessity of uncertainty analysis for hydrodynamic models. Furthermore, model calibration is of the utmost importance, because uncalibrated models tend to overestimate flooding volume and therefore result in larger diameters and retention volumes.

Modelling of moving bed biofilm membrane reactors (MBBMR) for on-site greywater treatment.

The study evaluates with a mechanistic model the pilot plant results of a combined moving bed biofilm process and membrane filtration (MBBMR) treating single household greywater. It mainly includes the simulation of reactor hydraulics, degradation of pollutants, development of biomass and settlement of sludge. Iterative calibration was made with steady-state results of a 10-month pilot test. The model shows good predictions of readily biodegradable chemical oxygen demand and ammonium removal, as well as biomass concentration on carriers and in suspension. Also, a sensitivity analysis was made which calculates the relative significance factor of each model coefficient and by this provides comparability with other studies. Simulation data and actually measured parameters show that the suggested process was rather independent of ambient temperatures and short-term load fluctuations. Obtained datasets and model structure could be of use for future designers, as well as sellers and users of this process for on-site greywater reclamation.

Modelling the urban water cycle as an integrated part of the city: a review.

In contrast to common perceptions, the urban water infrastructure system is a complex and dynamic system that is constantly evolving and adapting to changes in the urban environment, to sustain existing services and provide additional ones. Instead of simplifying urban water infrastructure to a static system that is decoupled from its urban context, new management strategies use the complexity of the system to their advantage by integrating centralised with decentralised solutions and explicitly embedding water systems into their urban form. However, to understand and test possible adaptation strategies, urban water modelling tools are required to support exploration of their effectiveness as the human-technology-environment system coevolves under different future scenarios. The urban water modelling community has taken first steps to developing these new modelling tools. This paper critically reviews the historical development of urban water modelling tools and provides a summary of the current state of integrated modelling approaches. It reflects on the challenges that arise through the current practice of coupling urban water management tools with urban development models and discusses a potential pathway towards a new generation of modelling tools.

Simplifying impact of urban development on sewer systems.

Linking urban development and urban drainage models is a more and more popular approach when impacts of pavement of urban areas on sewer system performance are evaluated. As such an approach is a difficult task, this is not a feasible procedure for everyday engineering practice. We propose an alternative method, based on a developed simple near-quadratic relationship, which directly translates change (increase or decrease) of paved area into a change in the return period (RP) of the design rainfall event or design rainfall intensity. This formula is simple to use and compatible with existing design guidelines. A further advantage is that the calculated design RP can also be used to communicate the impact of a change in impervious areas to stakeholders or the public community. The method is developed using a set of 250 virtual and two real-world case studies and hydrodynamic simulations. It is validated on a small catchment for which we compare system performance and redesigned pipe diameters. Of course such a simplification contains different uncertainties. But these uncertainties have to be seen in the context of overall uncertainties when trying to predict city development into the future. Hence it still is a significant advantage compared to today's engineering practice.

Adaptation of sewer networks using integrated rehabilitation management.

The urban water structure is aging and in need of rehabilitation. Further, the need to address future challenges (climate change, urban development) also arise lines. This study investigates if it is possible to combine rehabilitation and adaptation measures. To do so, we combined an urban development model, an urban drainage model and a rehabilitation model. A case study of a medium-sized alpine city with a sewer length of 228 km and a population of 125,431 was used to develop and apply this method. A priority model to pinpoint the structures in need of replacement was used. This model considered a deterioration model, vulnerability estimation and other influences. Further different rehabilitation rates and methods were examined. The urban development model used is a simplistic approach specifically tailored for the field of urban infrastructure management. Climate change is considered in terms of climate change factors. All these different influences together create scenarios for which the construction costs and the flooding volume are estimated and compared. Consequently the aim of this paper was to test to which degree it is possible to reduce urban flooding by adapting those parts of the network which require rehabilitation anyway. In our case study it could be reduced by 5%.

Effect of turbulent diffusion in modeling anaerobic digestion.

In this study, the impact of turbulent diffusion on mixing of biochemical reaction models is explored by implementing and validating different models. An original codebase called CHAD (Coupled Hydrodynamics and Anaerobic Digestion) is extended to incorporate turbulent diffusion and validate it against results from OpenFOAM with 2D Rayleigh-Taylor Instability and lid-driven cavity simulations. The models are then tested for the applications with Anaerobic Digestion - a widely used wastewater treatment method. The findings demonstrate that the implemented models accurately capture turbulent diffusion when provided with an accurate flow field. Specifically, a minor effect of chemical turbulent diffusion on biochemical reactions within the anaerobic digestion tank is observed, while thermal turbulent diffusion significantly influences mixing. By successfully implementing turbulent diffusion models in CHAD, its capabilities for more accurate anaerobic digestion simulations are enhanced, aiding in optimizing the design and operation of anaerobic digestion reactors in real-world wastewater treatment applications.

Lagrangian solver for coupling hydrodynamics with biokinetic conversion modelling in anaerobic digesters.

Conventional anaerobic digestion models used in wastewater treatment plants suffer from inaccuracies due to the limited consideration given to hydrodynamics within the digester tank. A solution to this is to combine computational fluid dynamics simulations with anaerobic models. This paper introduces a novel methodology in the form of a software toolbox that implements the standard anaerobic digestion model no.1 in C++ and can interface with particle-based Lagrangian simulations. This method provides significantly more insights into the biochemical conversion process by accounting for the impact of the hydrodynamics on the biochemical reactions. The paper presents the background of the method along with a conceptual and numerical verification. It also presents a case study of a 3D lab scale digester comparing the results from the solver with the standard anaerobic digestion model. This integrated approach can be used by operators and designers for optimisations and also for predictive modelling.

Estimating actual SARS-CoV-2 infections from secondary data.

Eminent in pandemic management is accurate information on infection dynamics to plan for timely installation of control measures and vaccination campaigns. Despite huge efforts in diagnostic testing of individuals, the underestimation of the actual number of SARS-CoV-2 infections remains significant due to the large number of undocumented cases. In this paper we demonstrate and compare three methods to estimate the dynamics of true infections based on secondary data i.e., (a) test positivity, (b) infection fatality and (c) wastewater monitoring. The concept is tested with Austrian data on a national basis for the period of April 2020 to December 2022. Further, we use the results of prevalence studies from the same period to generate (upper and lower bounds of) credible intervals for true infections for four data points. Model parameters are subsequently estimated by applying Approximate Bayesian Computation-rejection sampling and Genetic Algorithms. The method is then validated for the case study Vienna. We find that all three methods yield fairly similar results for estimating the true number of infections, which supports the idea that all three datasets contain similar baseline information. None of them is considered superior, as their advantages and shortcomings depend on the specific case study at hand.